Using the Ortho process

LegacyLegacy:

ArcGIS 10 is the last release of the stand-alone ArcGIS Image Server product. The image service definition (.ISDef) has been replaced by an improved geodatabase data model—the mosaic dataset—which can be published as an image service using the ArcGIS Server Image extension.

The Ortho process produces an orthorectified image based on a sensor definition and a terrain model.

The Ortho process is automatically included in the processing chain when a raster is added to an image service definition when using one of the Orthorectification raster types. Each of these raster types has a tab that allows you to specify the parameters for the Ortho process. The Ortho process is not generally added to an existing service.

About orthorectification

Across an unrectified image, there are inaccuracies due to distortions from the sensor and the earth's terrain. For example, since satellite imagery can be collected by scanning side to side along a path, this movement while collecting means that a spatially adjacent part of an image may have been collected from a nonadjacent part of the sensor, and although there are algorithms that bring this together before it is delivered to you, it can be improved. By orthorectifying an image, the distortions are geometrically removed, creating a planimetric image at every location with consistent scale across all parts. In other words, orthorectification is the process of stretching the image to match the spatial accuracy of a map by considering location, elevation, and sensor information.

You can produce an accurately orthorectified raster dataset using the sensor definition file, provided by the image vendor, and an accurate terrain model, such as a digital elevation model (DEM). The terrain model can be provided as a raster dataset, or a fixed value can be defined. However, in areas of diverse terrain, there are obvious advantages with regard to the accuracy of the orthorectification if a high-resolution terrain model is used. A scale and offset can also be applied to the elevation data to account for simple geoids or different units. To create the orthorectified image, the terrain model needs to be sampled, and a number of parameters must define the frequency and type of terrain sampling. By applying the information within the associated sensor definition file, you can determine the correct input pixel location for each ground pixel by applying the coefficient to the latitude, longitude, and height value of the pixel.

There are two types of fixed elevation values that can be used in the Ortho process: Fixed Z or Average Z. Fixed Z is an elevation value you enter on the Orthorectification Process Definition dialog box or the dialog box you use when you're adding the raster data. Average Z is an elevation value that is read from the sensor definition file.

Companies such as DigitalGlobe or Space Imaging deliver some of their images with the sensor definition file as rational polynomial coefficients (RPC),essentially, the abstraction of their proprietary camera model, which can be used to geometrically correct the image. Images from DigitalGlobe store RPC information in a <file name>.rpb file, and Space Imaging offers images with RPC information stored in a <file name>_rpc.txt file. Other companies, such as aerial imaging firms, may deliver their sensor definitions as part of their output from programs such as MATCH-AT and ISAT.

When using an elevation model, you need to be aware if the elevations referred to in the sensor definition use the ellipsoidal or orthometric heights, because the heights of your elevation model must use the equivalent measurement.

NoteNote:

For orthorectification using elevation data from any source, any value less than -998.0 and greater than 35000 is considered a NoData value and results in no image being processed around that area.

Ortho process parameters

The parameters on the Orthorectification Process Definition dialog box are the same as the parameters on the dialog boxes used when adding a raster dataset with one of the Orthorectification raster types.

Orthorectification Process Definition dialog box

The accuracy of the generated orthophoto is relative to the accuracy of the sensor definition, the accuracy of the digital elevation model, and how often the elevation model is sampled to represent the terrain. In flat areas, a lower sampling density is often adequate, whereas in areas of undulating terrain, a higher sampling density is required. Higher sampling densities increase the processing time. There are three values that control the sampling density:

During the Ortho process, the largest values specified for Minimum grid spacing of DEM and Anchor spacing are applied first, then the sampling is limited by the Max.number of cells value.

The terrain offset values are used to transform the terrain height values to those that are required by the sensor definition (if necessary). There are three values that can be specified:

To convert to approximate ellipsoidal heights for a local area, you can apply a scale and shift using the terrain offset and scale parameters. Alternatively, you can use the Convert Pixel Type process or Image Algebra process to convert the orthometric height using a geoid before applying the Ortho process. To do this, apply the following equation:

h = H + N
where,
h = ellipsoidal height
H = orthometric (geoid) height
N = geoidal separation

There are two additional parameters presented when adding aerial imagery that allow you to enter the clip percentages. The values for these parameters are used to clip a percentage from the edges of each frame (raster dataset) as well as for computing the approximate footprints for each. Separate control is given for forward and side overlap, since the forward overlap is often larger so that more can be clipped off. Typically there is at least 60 percent forward overlap and 25 percent side overlap. Clipping these overlapping edges allows artifacts to be removed along the edge of the frames at the cost of reducing the overlap available between the imagery.

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4/19/2011